Example data tracks

Project

Lamina mapping of various mESC-AID cell lines.

Introduction

In this document, I will create simple data tracks that illustrates the general data patterns. This is better than IGV snapshots.

Method

Simple R plotting.

Set-up

Set the parameters and list the data.

# Load dependencies - without warnings / messages
library(tidyverse)
library(GenomicRanges)
library(rtracklayer)
library(ggplot2)
library(reshape2)
library(RColorBrewer)
library(caTools)
library(wesanderson)
library(Gviz)
library(ggrastr)
library(colorspace)

# Prepare output 
output_dir <- "ts220117_DataTracks_log2ratios"
dir.create(output_dir, showWarnings = FALSE)

# Load previous input
input_dir <- "ts220113_effect_of_CTCF_depletion_on_LAD_borders/"
bin_size <- readRDS(file.path(input_dir, "bin_size.rds"))
damid <- readRDS(file.path(input_dir, "damid.rds"))
metadata <- readRDS(file.path(input_dir, "metadata_damid.rds"))

# Load previous input
input_dir <- "ts220113_GeneExpression"
genes <- readRDS(file.path(input_dir, "genes.rds"))
genes_fpkm <- readRDS(file.path(input_dir, "genes_fpkm_mean.rds"))

# Load chrom sizes
chrom.sizes <- read_tsv("~/mydata/data/genomes/mm10/mm10.chrom.sizes",
                        col_names = c("seqnames", "length"))

Set-up knitr output.

library(knitr)
opts_chunk$set(fig.width = 10, fig.height = 4, cache = T,
               dev=c('png', 'pdf'), fig.path = file.path(output_dir, "figures/")) 
pdf.options(useDingbats = FALSE)

List functions.

PlotDataTracks <- function(gr_norm_mean, name, samples,
                           centromeres = NULL,
                           plot_chr = "chr1", 
                           plot_start = 1, plot_end = 100e6,
                           smooth = 1, 
                           filter_samples = NULL,
                           lighten_negative = T,
                           color_list = NULL,
                           free_y = T) {
  
  # Get the scores for the samples
  tib <- as_tibble(gr_norm_mean) %>%
    dplyr::select(seqnames, start, end, 
                  contains("PT_0h"),
                  matches(name))
  
  if (! is.null(filter_samples)) {
    tib <- tib %>%
      dplyr::select(-matches(filter_samples))
  }
  
  if (smooth > 1) {
    tib <- tib %>%
      mutate_at(vars(contains("_")), 
                runmean, k = smooth)
  }
  
  # Filter for plotting window
  tib <- tib %>%
    filter(seqnames == plot_chr,
           start >= plot_start,
           end <= plot_end)
  
  # Gather
  tib_gather <- tib %>%
    gather(key, value, -seqnames, -start, -end) %>%
    separate(key, c("condition", "timepoint"), remove = F) %>%
    mutate(condition = factor(condition, levels = levels(samples$condition)),
           timepoint = factor(timepoint, levels = levels(samples$timepoint)),
           interaction = interaction(condition, timepoint)) %>%
    arrange(condition, timepoint) %>%
    mutate(interaction = factor(interaction, levels = unique(interaction)))
  tib_gather$fill_column <- tib_gather %>% pull(interaction)
  
  # Should negative values be a lighter shade of the color?
  if (lighten_negative) {
    tib_gather <- tib_gather %>%
      mutate(fill_column = interaction(fill_column,
                                       value < 0))
  }
  
  # Limits
  ylimits <- quantile(tib_gather$value, c(0.001, 0.999), na.rm = T)
  
  plt <- tib_gather %>%
    ggplot(aes(fill = fill_column)) +
    rasterize(geom_rect(aes(xmin = start / 1e6, xmax = end / 1e6, 
                            ymin = 0, ymax = value)),
              dpi = 300) +
    geom_hline(yintercept = 0, size = 0.5) +
    coord_cartesian(xlim = c(max(c(plot_start,
                                   min(tib_gather$start))) / 1e6,
                             min(c(plot_end,
                                   max(tib_gather$end))) / 1e6)) +
    xlab(paste0(plot_chr, " (Mb)")) +
    ylab("pA-DamID (z-score)") +
    scale_x_continuous(expand = c(0, 0)) + 
    scale_y_continuous(expand = c(0.025, 0.025)) +
    #scale_fill_gradient(low = "deepskyblue2", high = "deepskyblue4", 
    #                    guide = F) +
    theme_classic() +
    theme(aspect.ratio = 0.15)
  
  # Y-axis
  if (free_y) {
    plt <- plt + 
      facet_grid(interaction ~ ., scales = "free_y")
  } else {
    plt <- plt + 
      facet_grid(interaction ~ .)
  }
  
  # Colors
  if (! is.null(color_list)) {
    
    if (lighten_negative) {
      color_list <- c(color_list,
                      lighten(color_list, amount = 0.5))
    }
    
    colors <- levels(tib_gather$fill_column)
    
    color_list <- color_list[1:length(colors)]
    names(color_list) <- colors
    
    plt <- plt +
      scale_fill_manual(values = color_list, guide = "none")
    
  } else {
    
    color_list <- wes_palette("Zissou1", length(unique(tib_gather$fill_column)),
                              type = "continuous")
    
    plt <- plt + 
      scale_fill_manual(values = color_list, guide = "none")
    
  }
  
  
  plot(plt)
  
}

# Plot with Gviz
GetDataTracks <- function(damid, sample_names, fill = NULL, smooth = 3) {
  # Function to get DataTracks for every sample given
  
  damid_tmp <- damid
  mcols(damid_tmp) <- as_tibble(mcols(damid_tmp)) %>%
    dplyr::select(sample_names)
  
  # Smooth
  if (smooth > 1) {
    mcols(damid_tmp) <- as_tibble(mcols(damid_tmp)) %>%
      mutate_at(vars(contains("_")), 
                runmean, k = smooth)
  }
  
  ylimits <- quantile(as_tibble(mcols(damid_tmp)) %>%
                       gather(key, value) %>%
                        pull("value"), 
                      c(0.001, 0.999), na.rm = T)
  
  # Get DataTracks
  l <- list()
  for (i in 1:length(sample_names)) {
    s <- sample_names[i]
    f <- ifelse(is.null(fill), "darkblue", fill[i])
    # name shortcut
    n <- str_remove_all(s, "TCF|APL|AD21")
    tmp <- damid_tmp
    mcols(tmp) <- data.frame(score = mcols(damid_tmp)[, s])
    tmp <- DataTrack(tmp, name = n, type = "histogram", ylim = ylimits,
                     fill.histogram = f, col.histogram  = NA)
    l <- c(l, list(tmp))
  }
  l
}

CreatePlot <- function(chr, from, to, 
                       sample_names = c("PT_0h", 
                                        "WAPL_0h", "WAPL_6h", "WAPL_24h",
                                        "CTCFWAPL_0h", "CTCFWAPL_6h", "CTCFWAPL_24h"),
                       fill = c("black", 
                                "darkred", "darkred", "darkred",
                                "darkgreen", "darkgreen", "darkgreen"),
                       features = T, smooth = 3,
                       damid = damid) {
  
  if (features) {
    track_list <- list(ctcf_track,
                       genes_track,
                       gtrack)
  } else {
    track_list <- list(gtrack)
  }
  
  # plot tracks
  plotTracks(c(GetDataTracks(damid, 
                             sample_names,
                             fill = fill,
                             smooth = smooth),
               track_list),
             chromosome = chr,
             from = from,
             to = to,
             stacking = "dense",
             background.title = "transparent",
             fontcolor.title = "black",
             col.title = "black",
             col.axis = "black")
}


# gtrack & itrack
gtrack <- GenomeAxisTrack(labelPos = "below", 
                          col = "black", fontcolor = "black")
itrack <- IdeogramTrack(genome = "mm10")

# ctcf
ctcf_track <- DataTrack("Data_NQ/ChIP_NQ/CohesinFactors/2_Wapl-0D-antiCtcf_SF.3517_MQ15_sample.bw", 
                        name = "CTCF", type = "histogram", 
                        fill.histogram = "black", col.histogram = NA)

# genes
genes_track <- GeneRegionTrack(genes, shape = "arrow",
                               name = "Gene Model",
                               symbol = genes$gene_name)

0) Comparison with DamID

I want to include a figure where I compare pA-DamID with DamID, for the sake of completeness. Plot this here.

# Load the data
damid_old <- read_tsv("~/mydata/proj/3D_nucleus/results/ts180110_4DN_DataProcessing/results_mouse/normalized/bin-10kb/mESC_LMNB1-10kb-combined.norm.txt.gz", 
                      col_names = c("seqnames", "start", "end", "DamID_0h")) %>%
  mutate(start = start + 1)

## Rows: 272564 Columns: 4

## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: "\t"
## chr (1): seqnames
## dbl (3): start, end, DamID_0h

## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

damid_comparison <- as_tibble(damid) %>%
  dplyr::select(1:6) %>%
  full_join(damid_old) %>%
  drop_na() 

## Joining, by = c("seqnames", "start", "end")

damid_comparison <- as(damid_comparison, "GRanges")

# Fake metadata
metadata_comparison <- tibble(file = "",
                              sample = c("PT", "DamID"),
                              condition = factor(c("PT", "DamID")),
                              timepoint = factor("0h"), 
                              cell = "mESC")

# Plot tracks
PlotDataTracks(damid_comparison, name = "DamID", samples = metadata_comparison,
               plot_chr = "chr1", plot_start = 0, plot_end = 800e6,
               smooth = 25, color_list = c("grey30", "steelblue"))

# Gviz plot
CreatePlot(chr = "chr6",
           from = 1,
           to = 149736546, 
           sample_names = c("DamID_0h", "PT_0h"),
           fill = c("grey50", 
                    "steelblue"),
           features = F,
           smooth = 25, damid = damid_comparison)

## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(sample_names)` instead of `sample_names` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.

# Also, create scatterplot
as_tibble(mcols(damid_comparison)) %>%
  ggplot(aes(x = DamID_0h, y = PT_0h)) +
  geom_bin2d(bins = 100) +
  geom_abline(slope = 1, intercept = 0, col = "red") +
  ggtitle(paste0("R = ",
                 round(cor(damid_comparison$DamID_0h, 
                           damid_comparison$PT_0h,
                           method = "pearson"), 2))) +
  xlab("DamID (log2)") +
  ylab("pA-DamID (log2)") +
  scale_fill_gradient(low = "lightgrey", high = "black", name = "Count") +
  coord_cartesian(xlim = c(-5.5, 5), ylim = c(-3, 3)) +
  theme_bw() +
  theme(aspect.ratio = 1)

I also tried other plots, but I don’t like these.

CreatePlot(chr = "chr2",
           from = 3e6,
           to = 25e6, 
           sample_names = c("DamID_0h"),
           fill = c("grey30"),
           features = T,
           smooth = 5, damid = damid_comparison)

1) Plot tracks

Here, plot data tracks in R for the various depletions. These are useful for manuscript figures and presentations.

Plot several example regions.

PlotDataTracks(damid, 
               name = "0h", samples = metadata,
               plot_chr = "chr1", color_list = rep("steelblue", 7),
               plot_start = 0, plot_end = 800e6,
               smooth = 25)

## Warning: Removed 2016 rows containing missing values (geom_rect).

PlotDataTracks(damid,
               name = "0h|24h", samples = metadata,
               plot_chr = "chr1", color_list = rep("steelblue", 10),
               plot_start = 0, plot_end = 800e6,
               smooth = 25, filter_samples = "CTCFNQ|CTCF_")

## Warning: Removed 2880 rows containing missing values (geom_rect).

PlotDataTracks(damid,
               name = "PT|CTCFEL", samples = metadata,
               plot_chr = "chr1", color_list = rep("steelblue", 6),
               plot_start = 0, plot_end = 800e6,
               smooth = 25)

## Warning: Removed 1728 rows containing missing values (geom_rect).

PlotDataTracks(damid,
               name = "PT|CTCFNQ", samples = metadata,
               plot_chr = "chr1", color_list = rep("steelblue", 6),
               plot_start = 0, plot_end = 800e6,
               smooth = 25)

## Warning: Removed 1728 rows containing missing values (geom_rect).

PlotDataTracks(damid,
               name = "PT|^WAPL", samples = metadata,
               plot_chr = "chr1", color_list = rep("steelblue", 6),
               plot_start = 0, plot_end = 800e6,
               smooth = 25)

## Warning: Removed 1728 rows containing missing values (geom_rect).

PlotDataTracks(damid,
               name = "PT|CTCFWAPL", samples = metadata,
               plot_chr = "chr1", color_list = rep("steelblue", 6),
               plot_start = 0, plot_end = 800e6,
               smooth = 25)

## Warning: Removed 1728 rows containing missing values (geom_rect).

PlotDataTracks(damid,
               name = "PT|RAD21", samples = metadata,
               plot_chr = "chr1", color_list = rep("steelblue", 5),
               plot_start = 0, plot_end = 800e6,
               smooth = 25)

## Warning: Removed 1440 rows containing missing values (geom_rect).

And more.

PlotDataTracks(damid,
               name = "PT|CTCFEL", samples = metadata,
               plot_chr = "chr1", color_list = rep("steelblue", 6),
               plot_start = 130e6, plot_end = 800e6,
               smooth = 15)

## Warning: Removed 20 rows containing missing values (geom_rect).

PlotDataTracks(damid,
               name = "PT|CTCFEL", samples = metadata,
               plot_chr = "chr1", color_list = rep("steelblue", 6),
               plot_start = 152e6, plot_end = 158.5e6,
               smooth = 7)

PlotDataTracks(damid,
               name = "PT|CTCFNQ", samples = metadata,
               plot_chr = "chr1", color_list = rep("steelblue", 6),
               plot_start = 130e6, plot_end = 800e6,
               smooth = 15)

## Warning: Removed 18 rows containing missing values (geom_rect).

PlotDataTracks(damid,
               name = "PT|CTCFEL", samples = metadata,
               plot_chr = "chr3", color_list = rep("steelblue", 6),
               plot_start = 8e6, plot_end = 16e6,
               smooth = 3)

## Warning: Removed 73 rows containing missing values (geom_rect).

PlotDataTracks(damid,
               name = "PT|^WAPL", samples = metadata,
               plot_chr = "chr3", color_list = rep("steelblue", 6),
               plot_start = 8e6, plot_end = 16e6,
               smooth = 3)

## Warning: Removed 72 rows containing missing values (geom_rect).

PlotDataTracks(damid,
               name = "PT|CTCFWAPL", samples = metadata,
               plot_chr = "chr3", color_list = rep("steelblue", 6),
               plot_start = 8e6, plot_end = 16e6,
               smooth = 3)

## Warning: Removed 72 rows containing missing values (geom_rect).

PlotDataTracks(damid,
               name = "PT|CTCFEL", samples = metadata,
               plot_chr = "chr11", color_list = rep("steelblue", 6),
               plot_start = 30e6, plot_end = 800e6,
               smooth = 15)

## Warning: Removed 25 rows containing missing values (geom_rect).

PlotDataTracks(damid,
               name = "PT|^WAPL", samples = metadata,
               plot_chr = "chr11", color_list = rep("steelblue", 6),
               plot_start = 30e6, plot_end = 800e6,
               smooth = 15)

## Warning: Removed 24 rows containing missing values (geom_rect).

PlotDataTracks(damid,
               name = "PT|CTCFWAPL", samples = metadata, 
               plot_chr = "chr11", color_list = rep("steelblue", 6),
               plot_start = 30e6, plot_end = 800e6,
               smooth = 15)

## Warning: Removed 24 rows containing missing values (geom_rect).

PlotDataTracks(damid,
               name = "PT|RAD21", samples = metadata,
               plot_chr = "chr13", color_list = rep("steelblue", 5),
               plot_start = 70e6, plot_end = 80e6,
               smooth = 3)

PlotDataTracks(damid,
               name = "PT|RAD21", samples = metadata,
               plot_chr = "chr9", color_list = rep("steelblue", 5),
               plot_start = 80e6, plot_end = 95e6,
               smooth = 3)

## Warning: Removed 3 rows containing missing values (geom_rect).

PlotDataTracks(damid,
               name = "PT|RAD21", samples = metadata,
               plot_chr = "chr3", color_list = rep("steelblue", 5),
               plot_start = 140e6, plot_end = 146e6,
               smooth = 3)

## Warning: Removed 1 rows containing missing values (geom_rect).

PlotDataTracks(damid,
               name = "0h|24h", samples = metadata,
               plot_chr = "chr1", color_list = rep("steelblue", 10),
               plot_start = 0e6, plot_end = 50e6,
               smooth = 15, filter_samples = "CTCFNQ|CTCF_",
               free_y = F)

## Warning: Removed 2930 rows containing missing values (geom_rect).

PlotDataTracks(damid,
               name = "0h|24h", samples = metadata,
               plot_chr = "chr1", color_list = rep("steelblue", 10),
               plot_start = 0e6, plot_end = 35e6,
               smooth = 9, filter_samples = "CTCFNQ|CTCF_",
               free_y = F)

## Warning: Removed 2960 rows containing missing values (geom_rect).

PlotDataTracks(damid,
               name = "0h|24h", samples = metadata,
               plot_chr = "chr3", color_list = rep("steelblue", 10),
               plot_start = 0e6, plot_end = 36e6,
               smooth = 9, filter_samples = "CTCFNQ|CTCF_",
               free_y = F)

## Warning: Removed 3020 rows containing missing values (geom_rect).

PlotDataTracks(damid,
               name = "0h|24h", samples = metadata,
               plot_chr = "chr3", color_list = rep("steelblue", 10),
               plot_start = 4.5e6, plot_end = 45e6,
               smooth = 9, filter_samples = "CTCFNQ|CTCF_",
               free_y = F)

## Warning: Removed 70 rows containing missing values (geom_rect).

PlotDataTracks(damid,
               name = "0h|24h", samples = metadata,
               plot_chr = "chr3", color_list = rep("steelblue", 10),
               plot_start = 4.5e6, plot_end = 36e6,
               smooth = 9, filter_samples = "CTCFNQ|CTCF_",
               free_y = F)

## Warning: Removed 60 rows containing missing values (geom_rect).

2. Plot with Gviz

Again, try GViz for plotting.

# plot tracks
CreatePlot(chr = "chr3", 
           from = 8e6,
           to = 16e6, 
           damid = damid)

CreatePlot(chr = "chr2",
           from = 43e6,
           to = 60e6, 
           damid = damid)

# Chromosome-plots
CreatePlot(chr = "chr3",
           from = 0,
           to = 160039680, 
           sample_names = c("PT_0h",
                            "CTCFEL_0h", "CTCFEL_24h",
                            "WAPL_0h", "WAPL_24h",
                            "CTCFWAPL_0h", "CTCFWAPL_24h",
                            "RAD21_0h", "RAD21_24h"),
           fill = c("grey30", 
                    "red", "red",
                    "blue", "blue",
                    "darkgreen", "darkgreen",
                    "purple", "purple"),
           features = F,
           smooth = 31, 
           damid = damid)

CreatePlot(chr = "chr6",
           from = 0,
           to = 149736546, 
           sample_names = c("PT_0h",
                            "CTCFEL_0h", "CTCFEL_24h",
                            "WAPL_0h", "WAPL_24h",
                            "CTCFWAPL_0h", "CTCFWAPL_24h",
                            "RAD21_0h", "RAD21_24h"),
           fill = c("grey30", 
                    "red", "red2",
                    "blue", "blue2",
                    "green1", "green3",
                    "purple", "purple2"),
           features = F,
           smooth = 31, 
           damid = damid)

# Custom size plots
CreatePlot(chr = "chr6",
           from = 136e6,
           to = 149736546, 
           sample_names = c("PT_0h",
                            "CTCFEL_0h", "CTCFEL_24h",
                            "WAPL_0h", "WAPL_24h",
                            "CTCFWAPL_0h", "CTCFWAPL_24h",
                            "RAD21_0h", "RAD21_24h"),
           fill = c("grey30", 
                    "red", "red2",
                    "blue", "blue2",
                    "green1", "green3",
                    "purple", "purple2"),
           features = T,
           smooth = 3, 
           damid = damid)

CreatePlot(chr = "chr6",
           from = 136e6,
           to = 149736546, 
           sample_names = c("PT_0h",
                            "CTCFEL_0h", "CTCFEL_6h", 
                            "CTCFEL_24h", "CTCFEL_96h"),
           fill = c("grey30", 
                    "red", "red1",
                    "red2", "red3"),
           features = T,
           smooth = 3, 
           damid = damid)

CreatePlot(chr = "chr6",
           from = 136e6,
           to = 149736546, 
           sample_names = c("PT_0h",
                            "WAPL_0h", "WAPL_6h", 
                            "WAPL_24h", "WAPL_96h"),
           fill = c("grey30", 
                    "blue", "blue1",
                    "blue2", "blue3"),
           features = T,
           smooth = 3, 
           damid = damid)

Don’t like it.

Maybe with gene expression.

GetGeneDataTracks <- function(genes_fpkm, sample_names, ylimits,
                              fill = NULL, extend = 5000) {
  # Function to get DataTracks for every sample given
  
  genes_tmp <- genes
  mcols(genes_tmp) <- genes_fpkm %>%
    dplyr::select(sample_names)
  
  # Change GRanges object
  # start(genes_tmp) <- ifelse(strand(genes) == "+",
  #                            start(genes), end(genes) - extend)
  # end(genes_tmp) <- ifelse(strand(genes) == "+",
  #                          start(genes) + extend, end(genes))
  
  strand(genes_tmp) <- "*"
  
  # Get DataTracks
  l <- list()
  for (i in 1:length(sample_names)) {
    s <- sample_names[i]
    f <- ifelse(is.null(fill), "darkblue", fill[i])
    # name shortcut
    n <- str_remove_all(s, "TCF|APL|AD21")
    tmp <- genes_tmp
    mcols(tmp) <- data.frame(score = mcols(genes_tmp)[, s])
    tmp <- DataTrack(tmp, name = n, type = "histogram", ylim = ylimits,
                     fill.histogram = f, col.histogram  = NA)
    l <- c(l, list(tmp))
  }
  l
}

CreatePlotWithExpr <- function(chr, from, to, 
                               sample_names = c("CTCFWAPL_0h", "CTCFWAPL_24h",
                                                "RAD21_0h", "RAD21_24h"),
                               fill = c("darkgreen", "darkgreen",
                                        "purple2", "purple2"),
                               ylimits = c(0, 30)) {
  # plot tracks
  plotTracks(c(GetDataTracks(damid, 
                             sample_names,
                             fill = fill),
               GetGeneDataTracks(genes_fpkm, 
                             sample_names,
                             fill = fill,
                             ylimits = ylimits),
               list(genes_track,
                    gtrack)),
             chromosome = chr,
             from = from,
             to = to,
             #stacking = "dense",
             transcriptAnnotation = "symbol",
             background.title = "transparent",
             fontcolor.title = "black",
             col.title = "black",
             col.axis = "black")
}

# For differential genes
CreatePlotWithExpr(chr = "chr1",
                   from = 96e6,
                   to = 100e6, 
                   ylimits = c(0, 20))

CreatePlotWithExpr(chr = "chr9",
                   from = 89.5e6,
                   to = 92e6,
                   ylimits = c(0, 10))

CreatePlotWithExpr(chr = "chr18",
                   from = 45e6,
                   to = 50e6,
                   ylimits = c(0, 1))

No.

Conclusions

Good tracks for presentations. Just use custom R plotting, not GViz.

Session info

sessionInfo()

## R version 4.0.5 (2021-03-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
##  [1] grid      parallel  stats4    stats     graphics  grDevices utils    
##  [8] datasets  methods   base     
## 
## other attached packages:
##  [1] knitr_1.33           colorspace_2.0-2     Gviz_1.34.1         
##  [4] wesanderson_0.3.6    caTools_1.18.2       RColorBrewer_1.1-2  
##  [7] reshape2_1.4.4       rtracklayer_1.50.0   GenomicRanges_1.42.0
## [10] GenomeInfoDb_1.26.7  IRanges_2.24.1       S4Vectors_0.28.1    
## [13] BiocGenerics_0.36.1  forcats_0.5.1        stringr_1.4.0       
## [16] dplyr_1.0.7          purrr_0.3.4          readr_2.0.0         
## [19] tidyr_1.1.3          tibble_3.1.6         ggplot2_3.3.5       
## [22] tidyverse_1.3.1     
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1                backports_1.2.1            
##   [3] Hmisc_4.5-0                 BiocFileCache_1.14.0       
##   [5] plyr_1.8.6                  lazyeval_0.2.2             
##   [7] splines_4.0.5               BiocParallel_1.24.1        
##   [9] digest_0.6.28               ensembldb_2.14.1           
##  [11] htmltools_0.5.1.1           fansi_0.5.0                
##  [13] magrittr_2.0.1              checkmate_2.0.0            
##  [15] memoise_2.0.0               BSgenome_1.58.0            
##  [17] cluster_2.1.2               tzdb_0.1.2                 
##  [19] Biostrings_2.58.0           modelr_0.1.8               
##  [21] matrixStats_0.60.0          vroom_1.5.3                
##  [23] askpass_1.1                 prettyunits_1.1.1          
##  [25] jpeg_0.1-9                  blob_1.2.2                 
##  [27] rvest_1.0.1                 rappdirs_0.3.3             
##  [29] haven_2.4.1                 xfun_0.24                  
##  [31] crayon_1.4.2                RCurl_1.98-1.3             
##  [33] jsonlite_1.7.2              survival_3.2-11            
##  [35] VariantAnnotation_1.36.0    glue_1.5.0                 
##  [37] gtable_0.3.0                zlibbioc_1.36.0            
##  [39] XVector_0.30.0              DelayedArray_0.16.3        
##  [41] scales_1.1.1                DBI_1.1.1                  
##  [43] Rcpp_1.0.7                  progress_1.2.2             
##  [45] htmlTable_2.2.1             foreign_0.8-81             
##  [47] bit_4.0.4                   Formula_1.2-4              
##  [49] htmlwidgets_1.5.3           httr_1.4.2                 
##  [51] ellipsis_0.3.2              farver_2.1.0               
##  [53] pkgconfig_2.0.3             XML_3.99-0.6               
##  [55] nnet_7.3-16                 sass_0.4.0                 
##  [57] dbplyr_2.1.1                utf8_1.2.2                 
##  [59] tidyselect_1.1.1            labeling_0.4.2             
##  [61] rlang_0.4.12                AnnotationDbi_1.52.0       
##  [63] munsell_0.5.0               cellranger_1.1.0           
##  [65] tools_4.0.5                 cachem_1.0.5               
##  [67] cli_3.1.0                   generics_0.1.0             
##  [69] RSQLite_2.2.7               broom_0.7.9                
##  [71] evaluate_0.14               fastmap_1.1.0              
##  [73] yaml_2.2.1                  bit64_4.0.5                
##  [75] fs_1.5.0                    AnnotationFilter_1.14.0    
##  [77] xml2_1.3.2                  biomaRt_2.46.3             
##  [79] compiler_4.0.5              rstudioapi_0.13            
##  [81] curl_4.3.2                  png_0.1-7                  
##  [83] reprex_2.0.0                bslib_0.2.5.1              
##  [85] stringi_1.7.3               highr_0.9                  
##  [87] GenomicFeatures_1.42.3      lattice_0.20-44            
##  [89] ProtGenerics_1.22.0         Matrix_1.3-4               
##  [91] vctrs_0.3.8                 pillar_1.6.4               
##  [93] lifecycle_1.0.1             jquerylib_0.1.4            
##  [95] data.table_1.14.0           bitops_1.0-7               
##  [97] R6_2.5.1                    latticeExtra_0.6-29        
##  [99] gridExtra_2.3               codetools_0.2-18           
## [101] dichromat_2.0-0             assertthat_0.2.1           
## [103] SummarizedExperiment_1.20.0 openssl_1.4.4              
## [105] withr_2.4.2                 GenomicAlignments_1.26.0   
## [107] Rsamtools_2.6.0             GenomeInfoDbData_1.2.4     
## [109] hms_1.1.0                   rpart_4.1-15               
## [111] rmarkdown_2.11              MatrixGenerics_1.2.1       
## [113] biovizBase_1.38.0           Biobase_2.50.0             
## [115] lubridate_1.7.10            base64enc_0.1-3